Use of routinely collected data to classify planned mode of delivery among pregnancies with a previous cesarean delivery: a validation study.

Background: Counseling on the harms and benefits of a planned vaginal versus a planned repeat cesarean delivery often relies on observational studies using routinely collected (or administrative) data. However, the accuracy of planned (rather than actual) mode of delivery classifications in such data remains unknown. This study aimed to evaluate the validity of an administrative data-based algorithm to identify planned vaginal and planned cesarean deliveries among individuals with a previous cesarean.

Methods: An algorithm based on diagnostic and procedural codes was applied to records from the Nova Scotia Atlee Perinatal Database. Included were individuals with a previous cesarean eligible for a trial of labor between 2017 and 2019. We compared the classification of planned mode of delivery using the algorithm with that determined through review of a random sample of 200 medical charts. We estimated sensitivity, specificity, and predictive values with 95% confidence intervals (CI).

Results: Based on the chart review, 80 deliveries (40%) were planned vaginal deliveries. The algorithm had an estimated sensitivity of 99% (95% CI 93, 100%), specificity of 96% (95% CI 91, 99%), positive predictive value of 94% (95% CI 87, 98%), and negative predictive value of 99% (95% CI 95, 100%) for identifying planned vaginal deliveries.

Conclusions: An algorithm based on routinely collected data accurately classified planned vaginal and planned cesarean deliveries among individuals with a previous cesarean. These findings suggest that studies using similar algorithms to inform counseling on planned mode of delivery in this population are minimally impacted by misclassification of this data.